A number of different problems are typically encountered in hosting enterprise-wide IT. These problems include presence of multiple applications, heterogeneous servers, non-uniform utilization across servers, and complex service level agreement (SLA) and quality-of-service (QoS) requirements. Several different approaches have been proposed to address these issues.
US Patent Publication 2003-0225904 discloses a server load distribution apparatus, server load distribution program and server system. A server load distribution apparatus provided between a client and servers including a first power supply and a control unit activating the first power supply depending on a command, comprises a unit for receiving a data request packet from one of the client computers, a unit for transferring the received data request packet to the one of the servers, a unit for counting the number of responses per unit of time, the responses being responses of the servers to the data request packet, a unit for determining an optimal number of the servers which are in operation based on the data request packet and the number of the responses, and instruct the transfer unit to transfer the data request packet to one of the servers, and a unit for supply a plurality of commands for activating the first power supply to the control units.
US Patent Publication 2003-0005028 discloses a system and method for determining how many servers of at least one server configuration to be included at a service provider's site for supporting an expected workload. A method comprises receiving, into a capacity planning system, workload information representing an expected workload of client accesses of streaming media files from a site. The method further comprises the capacity planning system determining, for at least one server configuration, how many servers of the at least one server configuration to be included at the site for supporting the expected workload in a desired manner.
US Patent Publication 2005-0138170 discloses a method and apparatus for controlling the number of servers in a hierarchical resource environment. The invention relates to the control of servers which process client work requests in a computer system on the basis of resource consumption. Each server contains multiple server instances (also called “execution units”) which execute different client work requests in parallel. A workload manager determines the total number of server containers and server instances in order to achieve the goals of the work requests. The number of server instances started in each server container depends on the resource consumption of the server instances in each container and on the resource constraints, service goals and service goal achievements of the work units to be executed. At predetermined intervals during the execution of the work units the server instances are sampled to check whether they are active or inactive. Dependent on the number of active server instances the number of server address spaces and server instances is repeatedly adjusted to achieve an improved utilization of the available virtual storage and an optimization of the system performance in the execution of the application programs.
WIPO Publication WO/2004/012038 discloses near on-line servers. A dynamic state manager (DSM) for a server cloud manager (SCM) of a virtualized logical server cloud includes a resource definition, a rules module and a state manager engine. The resource definition incorporates information of the available physical and logical resources of the server cloud, including cost, priority, usage and demand information of the resources. The resource definition further incorporates dependencies and relationships between physical and logical resources. The rules module includes predetermined behavioral rules based on demand, usage, priority and cost information. The behavioral rules define optimized resource utilization of the resources of the server cloud. The state manager engine is linked to the resource definition and the rules module and cooperates with the SCM to apply the behavioral rules to achieve optimized resource utilization.